🛡️ ARAVM Security Report

📊 Executive Summary

⚔️ Attack Effectiveness Summary

🖼️ Visual Analysis Dashboard

Original → FGSM Attack → Patch Attack → Perturbation Magnified

🔥 Gradient Heatmap Analysis

Shows how the model's attention shifts under adversarial attack

📈 Noise Intensity Analysis (FGSM)

Testing model robustness across different perturbation budgets (ε)

📏 Detailed Robustness Metrics

🛡️ Defense Effectiveness

Testing defensive preprocessing against PGD attack

💡 Security Recommendations & Best Practices

• Adversarial Training: Retrain the model on adversarial examples generated during this analysis. This is the most effective defense, improving robustness by 40-60% on average.
• Input Preprocessing: Implement JPEG compression (quality 50-70) or spatial smoothing as a first line of defense. These are computationally cheap and can reduce attack success rate by 15-30%.
• Gradient Masking: Use defensive distillation or gradient regularization to make gradient-based attacks less effective.
• Ensemble Methods: Deploy multiple models with different architectures. An adversarial example crafted for one model is less likely to transfer to others.
• Input Validation: Implement runtime anomaly detection to flag suspicious inputs before classification. Monitor for unusual activation patterns.
• Rate Limiting: For API deployments, limit query rates to prevent attackers from probing model decision boundaries through iterative queries.
• Certified Defenses: Consider randomized smoothing for provable robustness guarantees within a certified L2 radius.

🔧 Technical Configuration